Does Auditor Objectivity Impact on the Relationship Between Information Technology and Efficiency and Effectiveness of Auditing: Evidence from Iraq
Bibliographic record
Abstract
This work aims to determine the effect of information technology on effectiveness and efficiency of auditors in the context of non-profit organizations in Iraq. Also to investigate the mediating influence on the relationship between information technology and the audit process' effectiveness and efficiency. The study framework was based on those reported in literature pertaining to the unified theory of acceptance and use of technology (UTAUT). The target population in this work are auditors of Iraqi non-profit organizations. 354 questionnaires were sent to the participants, however, only 262 were returned and deemed applicable for this work, which culminates in a 74.3 percent response rate. SPSS (Statistical Package of Social Science) version 24 was utilized to examine the research model. The data were processed using many statistical techniques, such as (Descriptive Statistics, Correlations Analysis and Multiple Regressions). The study found that there is a significant influence on the auditors' objectivity due to their role as a mediator on the relationship between IT and auditing non-profit organizations. The findings also confirmed that the auditors are required to upgrade their knowledge vis-à-vis computerized information systems to plan, direct, supervise, and review the performed tasks. The implications of these findings in this work are significant for managers and auditors, while also providing insights and encouraging evaluation of computerized accounting systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".